Integration of Genetic Familial Dependence Structure in Latent Class Models
Bibliographic record
Abstract
One of the main reasons for the slow progress in detecting susceptibility genes in complex diseases may be that the clinical diagnoses used as phenotypes are genetically heterogeneous. The general objective of this paper is to develop a latent class model to identify homogeneous disease sub-types based on multivariate disease measurements in pedigrees from genetic studies. Our hypothesis is that the resulting disease sub-types will be influenced by a small number of genes, that will thus be more easily detectable. Specifically, we extended latent class analysis to allow dependence between the latent disease class status of relatives within nuclear families as a function of their kinship. Such a dependence model is expected to capture the underlying Mendelian transmission of alleles within families. An EM algorithm maximizes the likelihood and a cross-validation approach selects the optimal model. Through a simulation study under a genetic disease class model, we show that taking into account familial dependence improves the classification of the individuals in their true classes, compared to a traditional model assuming independence. An application of our approach to a dataset from the Autism Genetics Research Exchange is also presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".